Virus Classification by Using a Fusion of Texture Analysis Methods

Transmission Electron Microscopy (TEM) is one of the most important tools for virus detection and identification. It enables researchers to study the characteristics of a virus in order to classify it into groups and families. From morphological characteristics of viruses in TEM images, this paper proposes a computer vision approach for virus classification. For this purpose, we compared ten texture descriptors methods and fused them in order to obtain a higher discriminative signature. We compared this signature to other works that addressed the same virus classification problem. The result achieved by our proposed fusion approach (87.27%) surpassed the highest accuracies of previous papers, thus proving to be very effective for discriminating virus in TEM images. Thus, we believe that our paper brings a relevant contribution to this important medical problem.